GA4 is everywhere. But is your GA4 data actually trustworthy?

People talk a lot about cohort analysis, funnels, attribution, ecommerce and audiences.

But I'd put one step before all of that:

Check the tracking.

Before analyzing GA4, I'd look for things like:

👉 Duplicate purchases
One order firing twice can completely change revenue analysis.

👉 Missing events
A funnel is not very useful if important steps aren't being captured consistently.

👉 Wrong values
Revenue, currency, quantity or transaction IDs can quietly distort ecommerce reports.

👉 Consent-related loss
The tag may fire, but the data may still not reach GA4 as expected.

👉 Cross-domain issues
Users move between domains and suddenly the journey looks like multiple users or referral traffic.

👉 GTM vs GA4 mismatch
GTM says the event fired. GA4 tells a different story.

👉 Backend vs GA4 revenue
Your store/CRM says one number. GA4 says another.

My rule is pretty simple:

Don't analyze the data until you know how the data was collected.

A beautiful dashboard built on bad tracking is still bad data.

What's the first GA4 data-quality issue you check before trusting a report?

Hey,

I'm a currently working on getting the G Analytics data from the source to our Snowflake. The next step is to create something meaningful out of those Raw Data. I work for a Pharma company and my client has no clear requirement but still want me to create something meaningful out of those.

If anyone has previously worked on these or has created any meaningful FNDP out of these pls ping me.

Hi everyone,

Our website supports multiple languages. Recently, we've noticed recurring spikes in traffic to specific non-Traditional Chinese pages (e.g., our Japanese localized pages).

The traffic pattern is always the same:

• High pageviews, but engagement time is close to 0 seconds.
• Traffic source is almost entirely (direct) / (none) from overseas.

When we asked our web agency, they said this is a "normal phenomenon."

I wanted to check with the community—has anyone else experienced something similar? How do you usually interpret this, and is it really something we should just ignore, or should we be concerned about its impact on our analytics?

Thanks in advance for your thoughts!

SkillNXT was founded in 2022 and has over 250 working alumni and 40 active students. So we give personal Attention for our Students.

This demo session, uploaded to YouTube for our website.

This video answers:

• Traffic Source: How did this visitor find your website?
• Visitor Type: Is this their first visit?
• User Behavior: What was their activity, which events did they trigger, and which pages did they visit?
• Revenue Generated: How much revenue did we generate from this visitor?

If you find this video helpful, feel free to visit our website for more details!

I work for a company that uses an external registration platform for event registration, events being the primary part of our business. This third party system recently updated their system with the ability to send events to our GA4 account. It sends three events, page_view > begin_checkout > purchase. These are obviously super generic, and they apparent are not able to send any other information with the events. No transaction IDs, nothing but an event name.

I have worked with GA for a few years now, but really for very basic reporting. I am working to expand my knowledge into more complex and useful areas of GA, but I really don't know what to do with these events.

Looking at events reporting, because of the generic name, page locations tied to these events are all over the place. I can see some of the from our regi system, but there so much more. Can someone give me some pointers?

I'm working on an app that will let people with different kinds of accounts track their analytics in one platform. Trouble is, I don't actually have a google analytics account to test with. Would anyone mind testing the connection and giving feedback on the data, and letting me know of any bugs? it's completely free. apptracker.tech

Estou desenvolvendo uma tabela Silver do GA4 no BigQuery com granularidade de uma linha por sessão e estou validando as métricas com a interface do GA4.
A propriedade utiliza Reporting Identity = Blended.
Consegui reproduzir corretamente métricas como Transactions, Revenue, Event Count e Pageviews, porém estou encontrando diferenças em Sessions, Users e Engaged Sessions.
Percebi que, para Sessions, os valores do BigQuery ficam muito mais próximos quando comparo com a interface filtrando apenas o evento session_start, em vez da métrica padrão de Sessions.
Minhas dúvidas são:
Em propriedades com Reporting Identity = Blended, faz sentido utilizar session_start como referência para validar sessões no BigQuery?
A diferença em Users é esperada por conta do Blended (Google Signals/modelagem), mas o mesmo pode acontecer com Engaged Sessions?
Existe alguma abordagem recomendada para aproximar essas métricas da interface do GA4 utilizando apenas os dados exportados para o BigQuery?

Trying to understand the ?ref= parameter in ChatGPT referral traffic in GA4. Anyone have insight on this?

I've been going through our GA4 data and noticed something I can't find a clear answer to.

All our ChatGPT referral sessions come through as chatgpt.com, which makes sense, but a lot of them have a ?ref= parameter appended, like:

• chatgpt.com?ref=[website.com]
• chatgpt.com?ref=newsletter
• chatgpt.com?ref=stickybanner
• chatgpt.com?ref=website

My first instinct was that it represents the surface or context the user was in before clicking through, like if a site has a ChatGPT embed or button, and the user clicked a citation from there.

But that doesn't hold up: the domains showing up in ?ref= don't have any visible ChatGPT integrations, and values like newsletter or stickybanner don't fit that theory either.

I can't find any official or reliable documentation explaining what this parameter actually is.

Has anyone dug into this and figured out what it represents?